2.1 Take a look at the data

names(iris)

## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width" 
## [5] "Species"

str(iris)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

summary(iris)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 

2.2 Let’s use a scatterplot to look at the relationship between petal length and width

# here's a basic plot command: plot(x, y)
# this says using the dataframe 'iris', plot 'petal width' on the x axis, and 'petal length' on the y axis
with(iris, 
     plot(Petal.Width, 
          Petal.Length))

# an alternative way to write this is to specify the dataframe iris, follwed by a dollar sign $, then the column name
plot(iris$Petal.Width, 
     iris$Petal.Length)

# I use this notation to help me keep track of multiple columns within different data frames

# another way to make the same plot is to use a tilde ~
# In R the tilde is used to separate left and right sides of a model formula
# so you could read this as: plot Petal Length as a function of Petal Width
plot(iris$Petal.Length ~ iris$Petal.Width)

# make this look nicer by adding axis labels for the x axis (xlab) and y axis (ylab), plus a title (main)
plot(iris$Petal.Length ~ iris$Petal.Width,
  xlab = "Petal Width (cm)", ylab = "Petal Length (cm)",
  main = "Data from three iris species"
)

# there is some clear separation in the points.  To see if those separate by species, make each species a separate color with col= (this only works if the variable you are using to definte the colors is categorical)
plot(iris$Petal.Length ~ iris$Petal.Width,
  xlab = "Petal Width (cm)", ylab = "Petal Length (cm)",
  main = "Data from three iris species", col = iris$Species
)

2.3 Use a boxplot to further visualize the differences between species

# the same formula can be used to make a boxplot
boxplot(iris$Petal.Length ~ iris$Species)

# then color in the boxes using col= and specifying the colors as a list
boxplot(iris$Petal.Length ~ iris$Species,
  col = c("black", "red", "green")
)

# if we have not yet mentioned it, you specify a list or vector of multiple items in R using the c() function, which stands for 'concatenate'

3.1 We will go back to the scatterplot and make our plots look nicer

## let's go back to the iris data scatterplot

# use pch= to change the plotting symbol (stands for plot character)
plot(iris$Petal.Length ~ iris$Petal.Width,
  xlab = "Iris Petal Width", ylab = "Iris Petal Length",
  main = "Data from three iris species", pch = 2
) # all points will be a triangle

plot(iris$Petal.Length ~ iris$Petal.Width,
  xlab = "Iris Petal Width", ylab = "Iris Petal Length",
  main = "Data from three iris species",
  pch = c(1, 2, 18)[unclass(iris$Species)]
)

# This works by using c(1, 2, 18) to create a vector,
# unclass(iris$Species) turns the list of species from a list of categories
# (a "factor" data type in R terminology) into a list of numbers, each representing a species:
c(1, 2, 18)[unclass(iris$Species)]

##   [1]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [24]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [47]  1  1  1  1  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
##  [70]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
##  [93]  2  2  2  2  2  2  2  2 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
## [116] 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18
## [139] 18 18 18 18 18 18 18 18 18 18 18 18

# do the same thing with assigning different colors to the 3 species
plot(iris$Petal.Length ~ iris$Petal.Width,
  xlab = "Iris Petal Width", ylab = "Iris Petal Length",
  main = "Data from three iris species",
  col = c("magenta", "dark green", "blue")[unclass(iris$Species)]
)

# add a legend: specify where it will be located, assign a unique title for each of the species, and specify the colors and plot characters (or symbol)
legend("topleft",
  legend = unique(iris$Species),
  col = c("magenta", "dark green", "blue"), pch = 1
)

# alternatively, you can use x,y coordinates to place the legend
legend(1.8, 4,
  legend = unique(iris$Species),
  col = c("magenta", "dark green", "blue"), pch = 1, bty = "n"
)

# saving plots:
# in Rstudio, you can save a plot very quickly by opening the 'Export' drop-down menu in the figure window, and selecting 'Copy to Clipboard'.  I use this to quickly past figures into a Word or powerpoint document as a way of taking notes while I work.  that way I can place 2 similar graphs side by side and look closely at them, instead of clicking back and forth.

# to make a printable, high quality figure, click on 'Save as PDF' or 'Save as Image' and then specify your size, orientation, and file you save to